Track accepted paper

CiteScore: 3.68ℹ
CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor: 2.850ℹImpact Factor:2017: 2.850The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

5-Year Impact Factor: 2.712ℹFive-Year Impact Factor:2017: 2.712To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

Source Normalized Impact per Paper (SNIP): 1.886ℹSource Normalized Impact per Paper (SNIP):2017: 1.886SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR): 1.028ℹSCImago Journal Rank (SJR):2017: 1.028SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Author StatsℹAuthor Stats:Publishing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in Scopus.

Call for Papers

Agri-Food is a complex industry, which challenges a wide range of processes, operations, and roles world-wide. Moreover, it is largely inefficient with an increasing number of demands and constraints being placed on it, making the need for Agri-Food innovative solutions ever more important. Agri-Food related stakeholders such as manufacturers, producers and retailers, as well as government and policy making departments, are intrinsically linked to globally key challenges in terms of defining and implementing sustainable solutions and, as it happens with all industries, technology plays a key role in the operations and decision-making of the Agri-Food sector.

With increased complexity of modern manufacturing systems, exponential growth of data has been seen in manufacturing industry. Efficient utilization of those big data would provide intelligence to infer the health conditions of manufacturing machines, for improved fault detection, diagnosis, prognosis, health management, and maintenance scheduling. Machine learning, as one of the prevailing data analytics methods, has been widely used to devise complex models and algorithms that lend themselves to derive knowledge from the data. As a branch of machine learning, deep learning attempts to model high level representations behind data and classify (predict) patterns via stacking multiple layers of information processing modules in hierarchical architectures, which has shown great potential for machine health condition inference and performance degradation prediction, especially in the big data era.